In [1]:
import numpy as np
import pandas as pd
import scanpy as sc
import scvelo as scv
import muon as mu
import cellrank as cr
In [2]:
from sinfo import sinfo
sinfo(dependencies=True)

The `sinfo` package has changed name and is now called `session_info` to become more discoverable and self-explanatory. The `sinfo` PyPI package will be kept around to avoid breaking old installs and you can downgrade to 0.3.2 if you want to use it without seeing this message. For the latest features and bug fixes, please install `session_info` instead. The usage and defaults also changed slightly, so please review the latest README at https://gitlab.com/joelostblom/session_info.
-----
cellrank    2.0.0
muon        0.1.5
numpy       1.22.4
pandas      1.5.3
scanpy      1.9.3
scvelo      0.2.5
sinfo       0.3.4
-----
PIL                         10.0.0
anndata                     0.8.0
anyio                       NA
arrow                       1.2.3
asttokens                   NA
attr                        23.1.0
attrs                       23.1.0
awkward                     2.3.1
awkward_cpp                 NA
babel                       2.12.1
backcall                    0.2.0
certifi                     2023.07.22
cffi                        1.15.1
charset_normalizer          3.2.0
comm                        0.1.3
cycler                      0.10.0
cython_runtime              NA
dateutil                    2.8.2
debugpy                     1.6.7
decorator                   5.1.1
defusedxml                  0.7.1
docrep                      0.3.2
executing                   1.2.0
fastjsonschema              NA
fqdn                        NA
h5py                        3.9.0
idna                        3.4
igraph                      0.10.6
ipykernel                   6.25.0
ipywidgets                  8.0.7
isoduration                 NA
jedi                        0.18.2
jinja2                      3.0.3
joblib                      1.3.1
json5                       NA
jsonpointer                 2.4
jsonschema                  4.18.4
jsonschema_specifications   NA
jupyter_events              0.6.3
jupyter_server              2.7.0
jupyterlab_server           2.24.0
kiwisolver                  1.4.4
leidenalg                   0.10.1
llvmlite                    0.40.1
markupsafe                  2.1.3
matplotlib                  3.7.1
mpl_toolkits                NA
mudata                      0.2.3
natsort                     8.4.0
nbformat                    5.9.1
numba                       0.57.1
overrides                   NA
packaging                   23.1
parso                       0.8.3
patsy                       0.5.3
pexpect                     4.8.0
pickleshare                 0.7.5
pkg_resources               NA
platformdirs                3.9.1
progressbar                 4.2.0
prometheus_client           NA
prompt_toolkit              3.0.39
psutil                      5.9.5
ptyprocess                  0.7.0
pure_eval                   0.2.2
pycparser                   2.21
pydev_ipython               NA
pydevconsole                NA
pydevd                      2.9.5
pydevd_file_utils           NA
pydevd_plugins              NA
pydevd_tracing              NA
pygam                       0.8.0
pygments                    2.15.1
pygpcca                     1.0.4
pynndescent                 0.5.10
pyparsing                   3.0.9
python_utils                NA
pythonjsonlogger            NA
pytz                        2023.3
referencing                 NA
requests                    2.31.0
rfc3339_validator           0.1.4
rfc3986_validator           0.1.1
rpds                        NA
scipy                       1.11.1
seaborn                     0.12.2
send2trash                  NA
six                         1.16.0
sklearn                     1.3.0
sniffio                     1.3.0
stack_data                  0.6.2
statsmodels                 0.14.0
texttable                   1.6.7
threadpoolctl               3.2.0
tornado                     6.3.2
tqdm                        4.65.0
traitlets                   5.9.0
typing_extensions           NA
umap                        0.5.3
uri_template                NA
urllib3                     2.0.4
wcwidth                     0.2.6
webcolors                   1.13
websocket                   1.6.1
wrapt                       1.15.0
yaml                        6.0.1
zmq                         25.1.0
zoneinfo                    NA
-----
IPython             8.14.0
jupyter_client      8.3.0
jupyter_core        5.3.1
jupyterlab          4.0.3
notebook            7.0.0
-----
Python 3.10.12 (main, Jul  5 2023, 18:54:27) [GCC 11.2.0]
Linux-5.19.0-35-generic-x86_64-with-glibc2.35
96 logical CPU cores, x86_64
-----
Session information updated at 2024-06-20 22:26
In [3]:
scv.set_figure_params(style='scvelo', dpi=100, dpi_save=510, frameon=None, vector_friendly=True, 
                         transparent=True, fontsize=7, figsize=(3,3), 
                         format='pdf', ipython_format='png2x')
In [3]:
bdata2 = sc.read_h5ad('../Hu_Organoid/srt.h5ad')
bdata2
Out[3]:
AnnData object with n_obs × n_vars = 8694 × 25373
    obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'percent.mt', 'percent.ribo', 'nCount_SCT', 'nFeature_SCT', 'SCT_snn_res.0.6', 'seurat_clusters', 'RNA_snn_res.0.6', 'celltype', 'celltype_rev'
    var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable'
    uns: 'neighbors'
    obsm: 'X_harmony', 'X_pca', 'X_umap'
    varm: 'HARMONY', 'PCs'
    obsp: 'distances'
In [5]:
bdata2.var_names
Out[5]:
Index(['AL627309.1', 'AL627309.5', 'AP006222.2', 'LINC01409', 'FAM87B',
       'LINC01128', 'LINC00115', 'FAM41C', 'AL645608.6', 'AL645608.2',
       ...
       'CHRDL1', 'DCX', 'AC006144.2', 'PRR32', 'FHL1', 'SOX3', 'FMR1NB',
       'AC236972.4', 'TTTY9B', 'AC023491.2'],
      dtype='object', length=25373)
In [6]:
sc.pl.umap(bdata2, color=['orig.ident', 'celltype'])

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
No description has been provided for this image
In [7]:
col_paired =["#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F"]
In [8]:
sc.pl.umap(
    bdata2,
    color="celltype",
    palette={
        "AT2 - activated": "#A6CEE3",
        "AT2 - mature": "#1F78B4",
        "RASC-AT2": "#B2DF8A",
        "Proliferating": "#33A02C",
        "AT2 intermediate":"#FB9A99",
        "Aberrant intermediate":"#E31A1C",
        "Basal": "#FDBF6F"
    }
)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
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In [9]:
full_feature_marker =[ 'NKX2-1','EPCAM','SCGB3A1','SCGB3A2', 'CEACAM6', 'SFTPB', 'SFTPC', 'SFTPD', 'LAMP3', 'NAPSA','SLC34A2', 'ABCA3','LRRK2','KRT8','CDKN1A', 'KRT17','FGFR2', 'ID2', 'AGER', 'RTKN2','COL4A4', 'MKI67','TP63', 'SLC2A1', 'CA9', 'VEGFA', 'CTNNB1', 'DUSP4', 'XBP1', 'VIM', 'HIF1A', 'EPAS1']
In [4]:
single_markers =['TP63', 'CEACAM6', 'ID2', 'DUSP4', 'VIM','VEGFA', 'LRRK2', 'FGFR2', 'SCGB3A2', 'SFTPC', 'LAMP3', 'MKI67', 'HIF1A', 'EPAS1']
In [12]:
sc.pl.dotplot(bdata2, full_feature_marker, groupby='celltype', standard_scale='var', dendrogram=True, save='organoid_dotplot.pdf')

WARNING: dendrogram data not found (using key=dendrogram_celltype). Running `sc.tl.dendrogram` with default parameters. For fine tuning it is recommended to run `sc.tl.dendrogram` independently.
WARNING: saving figure to file figures/dotplot_organoid_dotplot.pdf

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_dotplot.py:749: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
  dot_ax.scatter(x, y, **kwds)
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In [13]:
sc.pl.dotplot(bdata2, single_markers, groupby='celltype', standard_scale='var', dendrogram=True, cmap='viridis', figsize=[6,2], save='skinny_dotplot.pdf')

WARNING: saving figure to file figures/dotplot_skinny_dotplot.pdf

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_dotplot.py:749: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
  dot_ax.scatter(x, y, **kwds)
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In [17]:
b_data_sub = bdata2[bdata2.obs['celltype'].isin(['AT2 - activated', 'AT2 - mature', 'RASC-AT2', 'Proliferating', 'AT2 intermediate','Aberrant intermediate'])]
sc.pl.umap(
    b_data_sub,
    color="celltype")

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
No description has been provided for this image
In [18]:
b_data1 = b_data_sub[b_data_sub.obs['orig.ident'].isin(['Vehicle'])]
b_data2 = b_data_sub[b_data_sub.obs['orig.ident'].isin(['PT-2385'])]
data1_loom = scv.read('../Hu_Organoid/9224_1.loom')
data2_loom = scv.read('../Hu_Organoid/9224_2.loom')

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.
  utils.warn_names_duplicates("var")
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.
  utils.warn_names_duplicates("var")
In [19]:
scv.utils.clean_obs_names(b_data1)
scv.utils.clean_obs_names(data1_loom)
scv.utils.clean_obs_names(b_data2)
scv.utils.clean_obs_names(data2_loom)
#data1_loom.var_names_make_unique()
#data2_loom.var_names_make_unique()
In [20]:
shmeta1 = scv.utils.merge(b_data1, data1_loom)
shmeta2 = scv.utils.merge(b_data2, data2_loom)
In [8]:
sc.pl.umap(
    shmeta1,
    color="celltype",
    palette={
        "AT2 - activated": "#A6CEE3",
        "AT2 - mature": "#1F78B4",
        "RASC-AT2": "#B2DF8A",
        "Proliferating": "#33A02C",
        "AT2 intermediate":"#FB9A99",
        "Aberrant intermediate":"#E31A1C",
        }
)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
No description has been provided for this image
In [9]:
sc.pl.umap(
    shmeta2,
    color="celltype",
    palette={
        "AT2 - activated": "#A6CEE3",
        "AT2 - mature": "#1F78B4",
        "RASC-AT2": "#B2DF8A",
        "Proliferating": "#33A02C",
        "AT2 intermediate":"#FB9A99",
        "Aberrant intermediate":"#E31A1C",
        }
)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
No description has been provided for this image
In [25]:
sc.pl.umap(
    shmeta,
    color="orig.ident",
    palette={
        "Vehicle": "#000080",
        "PT-2385": "#FFA500"
        }
)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  cmap = copy(get_cmap(cmap))
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
No description has been provided for this image
In [210]:
sc.pl.embedding(shmeta, basis="umap", color=["orig.ident","celltype"], size=75, add_outline=True, save='total cleaned umap_20230815.pdf')

WARNING: saving figure to file figures/umaptotal cleaned umap_20230815.pdf

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  cmap = copy(get_cmap(cmap))
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
No description has been provided for this image
In [ ]:
#shmeta1 is VEH
#shmeta2 is PT
In [48]:
#scv.pp.filter_and_normalize(shmeta1, min_shared_counts=30, n_top_genes=3000)
#scv.pp.filter_and_normalize(shmeta2, min_shared_counts=30, n_top_genes=3000)
scv.pp.moments(shmeta1)
scv.pp.moments(shmeta2)

WARNING: Did not normalize X as it looks processed already. To enforce normalization, set `enforce=True`.
Normalized count data: spliced, unspliced.
computing neighbors
    finished (0:00:01) --> added 
    'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)
computing moments based on connectivities
    finished (0:00:04) --> added 
    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)
WARNING: Did not normalize X as it looks processed already. To enforce normalization, set `enforce=True`.
Normalized count data: spliced, unspliced.
computing neighbors
    finished (0:00:00) --> added 
    'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)
computing moments based on connectivities
    finished (0:00:05) --> added 
    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)
In [49]:
scv.tl.recover_dynamics(shmeta1, n_jobs=96)
scv.tl.recover_dynamics(shmeta2, n_jobs=96)

recovering dynamics (using 96/96 cores)

  0%|          | 0/1562 [00:00<?, ?gene/s]
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/tools/dynamical_model.py:721: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  np.array([dm.alpha, dm.beta, dm.gamma, dm.pars[:3]]) / dm.m[-1]
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/tools/dynamical_model.py:724: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  np.array([dm.t, dm.tau, dm.t_, dm.pars[4]]) * dm.m[-1]

    finished (0:01:45) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)
recovering dynamics (using 96/96 cores)

  0%|          | 0/1199 [00:00<?, ?gene/s]
    finished (0:01:07) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/tools/dynamical_model.py:721: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  np.array([dm.alpha, dm.beta, dm.gamma, dm.pars[:3]]) / dm.m[-1]
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/tools/dynamical_model.py:724: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  np.array([dm.t, dm.tau, dm.t_, dm.pars[4]]) * dm.m[-1]
In [50]:
scv.tl.velocity(shmeta1, mode='dynamical', n_jobs=96)
scv.tl.velocity(shmeta2, mode='dynamical', n_jobs=96)

computing velocities
    finished (0:00:08) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
computing velocities
    finished (0:00:07) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
In [51]:
scv.tl.velocity_graph(shmeta1,  n_jobs=96)
scv.tl.velocity_graph(shmeta2,  n_jobs=96)

computing velocity graph (using 96/96 cores)

  0%|          | 0/4168 [00:00<?, ?cells/s]
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/core/_parallelize.py:138: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  res = np.array(res) if as_array else res

    finished (0:00:32) --> added 
    'velocity_graph', sparse matrix with cosine correlations (adata.uns)
computing velocity graph (using 96/96 cores)

  0%|          | 0/4506 [00:00<?, ?cells/s]
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/core/_parallelize.py:138: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  res = np.array(res) if as_array else res

    finished (0:00:32) --> added 
    'velocity_graph', sparse matrix with cosine correlations (adata.uns)
In [52]:
scv.tl.latent_time(shmeta1)
scv.tl.latent_time(shmeta2)

computing terminal states
    identified 4 regions of root cells and 1 region of end points .
    finished (0:00:00) --> added
    'root_cells', root cells of Markov diffusion process (adata.obs)
    'end_points', end points of Markov diffusion process (adata.obs)
computing latent time using root_cells as prior
    finished (0:00:04) --> added 
    'latent_time', shared time (adata.obs)
computing terminal states
    identified 3 regions of root cells and 1 region of end points .
    finished (0:00:01) --> added
    'root_cells', root cells of Markov diffusion process (adata.obs)
    'end_points', end points of Markov diffusion process (adata.obs)
computing latent time using root_cells as prior
    finished (0:00:03) --> added 
    'latent_time', shared time (adata.obs)
In [53]:
scv.tl.velocity_confidence(shmeta1)
keys = 'velocity_length', 'velocity_confidence'
scv.pl.scatter(shmeta1, c=keys, cmap='coolwarm', perc=[5, 95])
scv.tl.velocity_confidence(shmeta2)
keys = 'velocity_length', 'velocity_confidence'
scv.pl.scatter(shmeta2, c=keys, cmap='coolwarm', perc=[5, 95])

--> added 'velocity_length' (adata.obs)
--> added 'velocity_confidence' (adata.obs)
--> added 'velocity_confidence_transition' (adata.obs)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:869: MatplotlibDeprecationWarning: The draw_all function was deprecated in Matplotlib 3.6 and will be removed two minor releases later. Use fig.draw_without_rendering() instead.
  cb.draw_all()
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:869: MatplotlibDeprecationWarning: The draw_all function was deprecated in Matplotlib 3.6 and will be removed two minor releases later. Use fig.draw_without_rendering() instead.
  cb.draw_all()
No description has been provided for this image 
--> added 'velocity_length' (adata.obs)
--> added 'velocity_confidence' (adata.obs)
--> added 'velocity_confidence_transition' (adata.obs)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:869: MatplotlibDeprecationWarning: The draw_all function was deprecated in Matplotlib 3.6 and will be removed two minor releases later. Use fig.draw_without_rendering() instead.
  cb.draw_all()
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:869: MatplotlibDeprecationWarning: The draw_all function was deprecated in Matplotlib 3.6 and will be removed two minor releases later. Use fig.draw_without_rendering() instead.
  cb.draw_all()
No description has been provided for this image
In [54]:
df = shmeta1.obs.groupby('celltype')[keys].mean().T
df.style.background_gradient(cmap='coolwarm', axis=1)
Out[54]:
celltype AT2 - activated AT2 - mature AT2 intermediate Aberrant intermediate Proliferating RASC-AT2
velocity_length 41.619279 34.521289 33.279471 36.225189 42.904032 42.157931
velocity_confidence 0.981757 0.725416 0.989794 0.988244 0.958688 0.942895
In [55]:
df2 = shmeta2.obs.groupby('celltype')[keys].mean().T
df2.style.background_gradient(cmap='coolwarm', axis=1)
Out[55]:
celltype AT2 - activated AT2 - mature AT2 intermediate Aberrant intermediate Proliferating RASC-AT2
velocity_length 40.014214 37.214495 32.386285 31.449455 42.837100 40.050007
velocity_confidence 0.992140 0.790536 0.988956 0.976112 0.976596 0.938393
In [122]:
scv.pl.scatter(shmeta1, color='latent_time', color_map='gnuplot', size=80, save='VEH latent_time.pdf')

saving figure to file ./figures/scvelo_VEH latent_time.pdf
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In [123]:
scv.pl.scatter(shmeta2, color='latent_time', color_map='gnuplot', size=80, save='PT latent_time.pdf')

saving figure to file ./figures/scvelo_PT latent_time.pdf
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In [251]:
scv.pl.velocity_embedding_stream(shmeta1, basis='umap', color='celltype', title='', legend_loc='right margin', save='VEH_velo_20230822.pdf')

saving figure to file ./figures/scvelo_VEH_velo_20230822.pdf
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In [253]:
scv.pl.velocity_embedding_stream(shmeta2, basis='umap', color='celltype', title='', legend_loc='right margin', save='PT_velo_20230822.svg')

saving figure to file ./figures/scvelo_PT_velo_20230822.svg
No description has been provided for this image
In [59]:
scv.tl.paga(shmeta1, groups='celltype', minimum_spanning_tree=False)
df = scv.get_df(shmeta1, 'paga/transitions_confidence', precision=5).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')

running PAGA using priors: ['velocity_pseudotime']
    finished (0:00:01) --> added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns)
    'paga/transitions_confidence', velocity transitions (adata.uns)
Out[59]:
  0 1 2 3 4 5
0 0 0 0 0 0 0
1 0.69 0 0 0 0 0
2 0 0 0 0 0 0
3 0 0 0.36 0 0 0
4 0.44 0.22 0 0 0 0.33
5 0.62 0 0 0.27 0 0
In [60]:
scv.tl.paga(shmeta2, groups='celltype', minimum_spanning_tree=False)
df = scv.get_df(shmeta2, 'paga/transitions_confidence', precision=5).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')

running PAGA using priors: ['velocity_pseudotime']
    finished (0:00:01) --> added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns)
    'paga/transitions_confidence', velocity transitions (adata.uns)
Out[60]:
  0 1 2 3 4 5
0 0 0 0 0 0 0.5
1 0.2 0 0 0 0 0.84
2 0 0 0 0.039 0 0
3 0 0 0 0 0 0
4 0.4 0.26 0.038 0 0 0
5 0 0 0 0.14 0 0
In [250]:
scv.set_figure_params(style='scvelo', dpi=100, dpi_save=350, frameon=None, vector_friendly=True, 
                         transparent=True, fontsize=7, figsize=(3,3), 
                         format='pdf', ipython_format='png2x')
In [98]:
        scv.pl.paga(shmeta1, threshold = 0.25,  basis='umap', size=75, alpha=.1,
        min_edge_width=2, node_size_scale=1.5, legend_loc='right',save='VEH PAGA alone_20230815.pdf')

WARNING: Invalid color key. Using grey instead.
saving figure to file ./figures/scvelo_VEH PAGA alone_20230815.pdf
No description has been provided for this image
In [99]:
scv.pl.paga(shmeta2, threshold = 0.25, basis='umap', size=75, alpha=.1,
            min_edge_width=2, node_size_scale=1.5, save='PT PAGA alone_20230815.pdf')

WARNING: Invalid color key. Using grey instead.
saving figure to file ./figures/scvelo_PT PAGA alone_20230815.pdf
No description has been provided for this image
In [20]:
top_genes = shmeta1.var['fit_likelihood'].sort_values(ascending=False).index[:300]
scv.pl.heatmap(shmeta1, var_names=top_genes, sortby='latent_time', col_color='celltype', figsize=[3,4] , n_convolve=100)
No description has been provided for this image
In [21]:
top_genes = shmeta2.var['fit_likelihood'].sort_values(ascending=False).index[:300]
scv.pl.heatmap(shmeta2, var_names=top_genes, sortby='latent_time', col_color='celltype', figsize=[3,4] , n_convolve=100)
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In [64]:
#shmeta1.write('./PT_velocity_RNA_scott_20230808_VEH_process.h5ad', compression='gzip')
In [65]:
#shmeta2.write('./PT_velocity_RNA_scott_20230808_PT_process.h5ad', compression='gzip')
In [4]:
shmeta1 = scv.read('./PT_velocity_RNA_scott_VEH_20230822.h5ad')
In [5]:
shmeta2 = scv.read('./PT_velocity_RNA_scott_PT_20230822.h5ad')
In [6]:
vk1 = cr.kernels.VelocityKernel(shmeta1)
vk2 = cr.kernels.VelocityKernel(shmeta2)
In [7]:
vk1.compute_transition_matrix(model = 'monte_carlo', n_jobs=-1)
vk2.compute_transition_matrix(model = 'monte_carlo', n_jobs=-1)

  0%|          | 0/4168 [00:00<?, ?cell/s]
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  0%|          | 0/4506 [00:00<?, ?cell/s]
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Out[7]:
VelocityKernel[n=4506, model='monte_carlo', similarity='correlation', softmax_scale=1.267, n_samples=1000, seed=None]
In [8]:
from cellrank.kernels import ConnectivityKernel
ck1 = cr.kernels.ConnectivityKernel(shmeta1)
ck1.compute_transition_matrix()
Out[8]:
ConnectivityKernel[n=4168, dnorm=True, key='connectivities']
In [9]:
ck2 = cr.kernels.ConnectivityKernel(shmeta2)
ck2.compute_transition_matrix()
Out[9]:
ConnectivityKernel[n=4506, dnorm=True, key='connectivities']
In [10]:
from cellrank.kernels import PseudotimeKernel
pk1 = PseudotimeKernel(shmeta1, time_key="latent_time")
pk1.compute_transition_matrix()
pk2 = PseudotimeKernel(shmeta2, time_key="latent_time")
pk2.compute_transition_matrix()

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Out[10]:
PseudotimeKernel[n=4506, dnorm=False, scheme='hard', frac_to_keep=0.3]
In [11]:
pk1
Out[11]:
PseudotimeKernel[n=4168, dnorm=False, scheme='hard', frac_to_keep=0.3]
In [12]:
pk2
Out[12]:
PseudotimeKernel[n=4506, dnorm=False, scheme='hard', frac_to_keep=0.3]
In [13]:
combined_kernel_1 = 0.5 * vk1 + 0.5* ck1
In [14]:
combined_kernel_2 = 0.5 * vk2 + 0.5 * ck2
In [15]:
combined_kernel_1
Out[15]:
(0.5 * VelocityKernel[n=4168, model='monte_carlo', similarity='correlation', softmax_scale=1.278, n_samples=1000, seed=None] + 0.5 * ConnectivityKernel[n=4168, dnorm=True, key='connectivities'])
In [16]:
combined_kernel_2
Out[16]:
(0.5 * VelocityKernel[n=4506, model='monte_carlo', similarity='correlation', softmax_scale=1.267, n_samples=1000, seed=None] + 0.5 * ConnectivityKernel[n=4506, dnorm=True, key='connectivities'])
In [17]:
vk1.plot_projection(color="celltype", legend_loc='right margin', save='VEH cellrank trajectory_vk1_montecarlo_20230822.svg')

saving figure to file ./figures/scvelo_VEH cellrank trajectory_vk1_montecarlo_20230822.svg
No description has been provided for this image
In [18]:
combined_kernel_1.plot_projection(color="celltype", legend_loc='right margin', save='VEH cellrank trajectory_combinedKernal1_monteCarlo_20230822_final.pdf')

figure cannot be saved as pdf, using png instead.
saving figure to file ./figures/scvelo_VEH cellrank trajectory_combinedKernal1_monteCarlo_20230822_final.png
No description has been provided for this image
In [19]:
vk2.plot_projection(color="celltype", legend_loc='right margin', save='PT cellrank trajectory_vk2_montecarlo_20230822.svg')

saving figure to file ./figures/scvelo_PT cellrank trajectory_vk2_montecarlo_20230822.svg
No description has been provided for this image
In [20]:
combined_kernel_2.plot_projection(color="celltype", legend_loc='right margin', save='PT cellrank trajectory_combinedKernal2_monte_carlo_20230822.pdf')

saving figure to file ./figures/scvelo_PT cellrank trajectory_combinedKernal2_monte_carlo_20230822.pdf
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In [21]:
g1 = cr.estimators.GPCCA(vk1)
#g1 = cr.estimators.GPCCA(combined_kernel_1)
In [22]:
g2 = cr.estimators.GPCCA(vk2)
#g2 = cr.estimators.GPCCA(combined_kernel_2)
In [61]:
print(g1)

GPCCA[kernel=VelocityKernel[n=4168], initial_states=None, terminal_states=None]
In [62]:
g1.compute_schur()
g1.plot_spectrum(real_only=True)

WARNING: Unable to import `petsc4py` or `slepc4py`. Using `method='brandts'`
WARNING: For `method='brandts'`, dense matrix is required. Densifying
No description has been provided for this image
In [65]:
g2.compute_schur()
g2.plot_spectrum(real_only=True)

WARNING: Unable to import `petsc4py` or `slepc4py`. Using `method='brandts'`
WARNING: For `method='brandts'`, dense matrix is required. Densifying
No description has been provided for this image
In [25]:
#using n=3 states for veolcity analysis only with different predicted terminal states with veolocity kernal only
g1.fit(cluster_key="celltype", n_states=4)
#using n=7 for combined kernal analysis
#g1.fit(cluster_key="celltype", n_states=7)
g1.plot_macrostates(which="all", discrete=False, legend_loc="right")
g2.fit(cluster_key="celltype", n_states=4)
#g2.fit(cluster_key="celltype", n_states=7)
g2.plot_macrostates(which="all", discrete=False, legend_loc="right")

WARNING: Unable to import `petsc4py` or `slepc4py`. Using `method='brandts'`
WARNING: For `method='brandts'`, dense matrix is required. Densifying

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
No description has been provided for this image 
WARNING: Unable to import `petsc4py` or `slepc4py`. Using `method='brandts'`
WARNING: For `method='brandts'`, dense matrix is required. Densifying

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
No description has been provided for this image
In [26]:
g1.plot_macrostates(which="all", discrete=False, legend_loc="right", save='VEH macrostates 8-22.pdf')
g2.plot_macrostates(which="all", discrete=False, legend_loc="right", save='PT macrostates 8-22.pdf')

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(

saving figure to file ./figures/scvelo_VEH macrostates 8-22.pdf
No description has been provided for this image 
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(

saving figure to file ./figures/scvelo_PT macrostates 8-22.pdf
No description has been provided for this image
In [103]:
g1.predict_terminal_states()
#setting terminal states for n=7 states
#g1.set_terminal_states(states=["AT2 intermediate", "Aberrant intermediate", "AT2 - activated", "AT2 - mature"])
g1.plot_macrostates(which="terminal", legend_loc="right", s=100)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
No description has been provided for this image
In [147]:
g1.plot_macrostates(which="terminal", discrete=False, add_outline=True, save = 'VEH terminal state prediction_outline.pdf')

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1269: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1270: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1269: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1270: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1269: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'vmin', 'vmax' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1270: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'vmin', 'vmax' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)

saving figure to file ./figures/scvelo_VEH terminal state prediction_outline.pdf
No description has been provided for this image
In [105]:
g2.predict_terminal_states()
#g2.set_terminal_states(states=["AT2 intermediate_1", "Aberrant intermediate", "AT2 - activated", "AT2 - mature"])
g2.plot_macrostates(which="terminal", legend_loc="right", s=100)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
No description has been provided for this image
In [148]:
g2.plot_macrostates(which="terminal", discrete=False, add_outline=True, save = 'PT terminal state prediction_outline.pdf')

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1269: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1270: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1269: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1270: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1269: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'vmin', 'vmax' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/utils.py:1270: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'vmin', 'vmax' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)

saving figure to file ./figures/scvelo_PT terminal state prediction_outline.pdf
No description has been provided for this image
In [107]:
g1.predict_initial_states(allow_overlap=True)
g2.predict_initial_states(allow_overlap=True)
g1.plot_macrostates(which="initial", legend_loc="right", s=100)
g2.plot_macrostates(which="initial", legend_loc="right", s=100)

/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
No description has been provided for this image 
/home/mccalas1/miniconda3/envs/py3_10/lib/python3.10/site-packages/scvelo/plotting/scatter.py:614: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
No description has been provided for this image
In [133]:
g1.compute_fate_probabilities()
g1.plot_fate_probabilities(same_plot=False, save = 'VEH abs fate probs.pdf')

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saving figure to file ./figures/scvelo_VEH abs fate probs.pdf
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In [134]:
g2.compute_fate_probabilities()
g2.plot_fate_probabilities(same_plot=False, save = 'PT abs fate probs.pdf')

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saving figure to file ./figures/scvelo_PT abs fate probs.pdf
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In [126]:
cr.pl.aggregate_fate_probabilities(
    shmeta1,
    mode="clustermap",
    cluster_key="celltype",
    save='VEH cluster abs prob.pdf'
    )
No description has been provided for this image
In [127]:
cr.pl.aggregate_fate_probabilities(
    shmeta2,
    mode="clustermap",
    cluster_key="celltype",
    save='PT cluster abs prob.pdf'
    )
No description has been provided for this image
In [145]:
delta_df = g1.compute_lineage_drivers(
    lineages=["Aberrant intermediate"], cluster_key="celltype"
)
delta_df.head(30)
Out[145]:
Aberrant intermediate_corr Aberrant intermediate_pval Aberrant intermediate_qval Aberrant intermediate_ci_low Aberrant intermediate_ci_high
CA9 0.783764 0.0 0.0 0.771768 0.795202
IGFBP2 0.738087 0.0 0.0 0.723950 0.751605
NDRG1 0.727449 0.0 0.0 0.712832 0.741434
HILPDA 0.724418 0.0 0.0 0.709665 0.738535
KISS1R 0.692556 0.0 0.0 0.676418 0.708029
TXNIP 0.690897 0.0 0.0 0.674689 0.706439
CLDN4 0.675117 0.0 0.0 0.658249 0.691308
AKR7A3 0.648376 0.0 0.0 0.630426 0.665634
CP 0.646296 0.0 0.0 0.628263 0.663634
TM4SF1 0.645281 0.0 0.0 0.627209 0.662659
MT3 0.630905 0.0 0.0 0.612273 0.648838
IGFBP5 0.625872 0.0 0.0 0.607047 0.643996
SERPINA1 0.622676 0.0 0.0 0.603729 0.640920
PGK1 0.613611 0.0 0.0 0.594323 0.632194
ATF3 0.611519 0.0 0.0 0.592152 0.630179
HMGB3 0.610797 0.0 0.0 0.591404 0.629484
SOX4 0.610502 0.0 0.0 0.591097 0.629200
JUN 0.603691 0.0 0.0 0.584035 0.622639
VIM 0.599580 0.0 0.0 0.579774 0.618678
WFDC2 0.597151 0.0 0.0 0.577256 0.616337
TFF3 0.593412 0.0 0.0 0.573382 0.612734
WDR86 0.588604 0.0 0.0 0.568401 0.608098
TCIM 0.586505 0.0 0.0 0.566227 0.606073
TREM1 0.586390 0.0 0.0 0.566108 0.605963
CDKN1C 0.580961 0.0 0.0 0.560487 0.600726
MUC20 0.572335 0.0 0.0 0.551558 0.592401
ENO1 0.571917 0.0 0.0 0.551126 0.591998
GPI 0.563490 0.0 0.0 0.542409 0.583862
BNIP3 0.561554 0.0 0.0 0.540407 0.581992
ERO1A 0.560882 0.0 0.0 0.539712 0.581343
In [146]:
delta_df.to_csv('Abberant lineage drivers 8-27.csv')
In [ ]:
In [26]:
model = cr.models.GAM(shmeta1)
cr.pl.gene_trends(
    shmeta1,
    model=model,
    data_key="X",
    genes=["VIM", "SCGB3A2", "SFTPC"],
    lineages= "Aberrant intermediate",
    ncols=3,
    time_key="latent_time",
    hide_cells=True,
    figsize=(2, 3),
    n_test_points=200,
    save= 'vim_3A2_SFTPC model.pdf'
    )

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In [130]:
delta_df = g2.compute_lineage_drivers(
    lineages=["AT2 intermediate"], cluster_key="celltype", clusters="RASC-AT2"
)
delta_df.head(20)
Out[130]:
AT2 intermediate_corr AT2 intermediate_pval AT2 intermediate_qval AT2 intermediate_ci_low AT2 intermediate_ci_high
CA9 0.434164 1.256992e-66 2.846585e-62 0.390305 0.476057
PGK1 0.427839 1.761362e-64 1.994390e-60 0.383700 0.470026
TFF3 0.415950 1.400733e-60 1.057367e-56 0.371299 0.458682
PFKP 0.413809 6.772110e-60 3.834030e-56 0.369067 0.456638
CXCL17 0.411407 3.909683e-59 1.770774e-55 0.366563 0.454343
FAM162A 0.390655 7.812885e-53 2.948843e-49 0.344964 0.434498
WFDC2 0.376066 1.089926e-48 3.085309e-45 0.329808 0.420522
ENO1 0.371707 1.705125e-47 4.290473e-44 0.325285 0.416342
HILPDA 0.365580 7.548058e-46 1.709333e-42 0.318930 0.410463
BNIP3 0.363883 2.123106e-45 4.370896e-42 0.317170 0.408834
GAPDH 0.363365 2.905914e-45 5.483944e-42 0.316634 0.408337
ASCL1 0.362543 4.780795e-45 8.328144e-42 0.315781 0.407547
LDHA 0.360167 1.996287e-44 3.229136e-41 0.313319 0.405266
CLDN3 0.359456 3.053904e-44 4.610580e-41 0.312582 0.404583
TPI1 0.352559 1.777196e-42 2.515399e-39 0.305439 0.397958
PKM 0.348492 1.856975e-41 2.473709e-38 0.301228 0.394048
MIF 0.341220 1.124813e-39 1.415140e-36 0.293705 0.387053
SCD 0.325403 5.699539e-36 6.146274e-33 0.277362 0.371821
CST3 0.324671 8.348704e-36 8.593853e-33 0.276607 0.371116
XBP1 0.321878 3.550330e-35 3.495686e-32 0.273723 0.368422
In [146]:
shmeta1.write('./PT_velocity_RNA_scott_VEH_20230822.h5ad', compression='gzip')
In [147]:
shmeta2.write('./PT_velocity_RNA_scott_PT_20230822.h5ad', compression='gzip')
In [84]:
model = cr.models.GAM(shmeta1, n_knots=6)
cr.pl.gene_trends(
    shmeta1,
    model=model,
    data_key="X",
    genes=["VIM", "SCGB3A2", "SFTPC"],
    ncols=1,
    time_key="latent_time",
    same_plot=True,
    hide_cells=True,
    figsize=(4, 8),
    n_test_points=200,
    )

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